22 citations · 75 across the 9 of their papers we have counts for
9 papers · 1 filter
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Jinming Duan, Ghalib Bello, Jo Schlemper +7
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image…
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
Jo Schlemper, Ozan Oktay, Michiel Schaap +4
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs…
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
Jinming Duan, Jo Schlemper, Wenjia Bai +6
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method ex…
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Chen Qin, Wenjia Bai, Jo Schlemper +4
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a nov…
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
Jo Schlemper, Guang Yang, Pedro Ferreira +9
Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the…